Tuesday, January 19, 2010

Classes started this week for the spring semester at Boston College where I am teaching a class on marketing analytics to MBA students at the Carroll School of Management. The class makes heavy use of Gordon's book, Data Analysis Using SQL and Excel and the data that accompanies it. Since the local database is Oracle, I have at long last added Oracle load scripts to the book's companion page.

Due to laziness, my method of creating the Oracle script was to use the existing MySQL script and edit bits that didn't work in Oracle. As it happens, the MySQL scripts worked pretty much as-is to load the tab-delimited data into Oracle tables using Oracle's sqlldr utility. One case that did not work taught me something about the danger of mixing tab-delimited data with input formats in sqlldr. Even though it has nothing to do with data mining, as a public service, that will be the topic of my next post.

Preview: Something that works perfectly well when your field delimiter is comma, fails mysteriously when it is tab.

Saturday, January 9, 2010

Over the past three months, I have been teaching myself enough Hadoop to get comfortable with using the environment for analytic purposes.

There has been a lot of commentary about Hadoop/MapReduce versus relational databases (such as the articles referenced in my previous post on the subject). I actually think this discussion is misplaced because comparing open-source software with commercial software aligns people on "religious" grounds. Some people will like anything that is open-source. Some people will attack anything that is open-source (especially people who work for commercial software vendors). And, the merits of real differences get lost. Both Hadoop and relational databases are powerful systems for analyzing data, and each has its own distinct set of advantages and disadvantages.

Instead, I think that Hadoop should be compared to a parallel dataflow style of programming. What is a dataflow style of programming? It is a style where we watch the data flow through different operations, forking and combining along the way, to achieve the desired goal. Not only is a dataflow a good way to understand relational databases (which is why I introduce it in Chapter 1 of Data Analysis Using SQL and Excel), but the underlying engines that run SQL queries are dataflow engines.

Parallel dataflows extend dataflow processing to grid computing. To my knowledge, the first commercial tool that implements parallel dataflows was developed by Ab Initio. This company was a spin-off from a bleeding edge parallel supercomputer vendor called Thinking Machines that went bankrupt in 1994. As a matter of full disclosure: Ab Initio was actually formed from the group that I worked for at Thinking Machines. Although they are very, very, very resistant to sharing information about their technology, I am rather familiar it. I believe that the only publicly available information about them (including screen shots) is published in our book Mastering Data Mining: The Art and Science of Customer Relationship Management.

I am confident that Apache has at least one dataflow project, since when I google "dataflow apache" I get a pointer to the Dapper project. My wish, however, is that Hadoop were the parallel dataflow project.

Much of what Hadoop does goes unheralded by the typical MapReduce user. On a massively parallel system, Hadoop keeps track of the different parts of an HDFS file and, when the file is being used for processing, Hadoop does its darndest to keep the processing local to each file part being processed. This is great, since data locality is key to achieving good performance.

Hadoop also keeps track of which processors and disk systems are working. When there is a failure, Hadoop tries again, insulating the user from sporadic hardware faults.

Hadoop also does a pretty good job of shuffling data around, between the map and reduce operations. The shuffling method -- sorting, send, and sort again -- may not be the most efficient but it is quite general.

Alas, there are several things that Hadoop does not do, at least when accessed through the MapReduce interface. Supporting these features would allow it move beyond the MapReduce paradigm, giving it the power to support more general parallel dataflow constructs.

The first thing that bothers me about Hadoop is that I cannot easily take a text file and just copy it with the Map/Reduce primitives. Copying a file seems like something that should be easy. The problem is that a key gets generated during the map processing. The original data gets output with a key prepended, unless I do a lot of work to parse out the first field and use it as a key.

Could the context.write() function be overloaded with a version that does not output a key? Perhaps this would only be possible in the reduce phase, since I understand the importance of the key for going from map to reduce.

A performance issue with Hadoop is the shuffle phase between the map and the reduce. As I mentioned earlier, the sort-send-sort process is quite general. Alas, though, it requires a lot of work. An alternative that often works well is simply hashing. To maintain the semantics of map-reduce, I think this would be hash-send-combine or hash-send-sort. The beauty of using hashing is that the data can be sent to its destination while the map is still processing it. This allows concurrent use of the processing and network during this operation.

And, speaking of performance, why does the key have to go before the data? Why can't I just point to a sequence of bytes and use that for the key? This would enable a programming style that doesn't spend so much time parsing keys and duplicating information between values and keys.

Perhaps the most frustrating aspect of Hadoop is the MapReduce framework itself. The current version allows processing like (M+)(R)(M*). What this notation means is that the processing starts with one or more map jobs, goes to a reduce, and continues with zero or more map jobs.

THIS IS NOT GENERAL ENOUGH! I would like to have an arbitrary number of maps and reduces connected however I like. So, one map could feed two different reduces, each having different keys. At the same time, one of the reduces could feed another reduce without having to go through an intermediate map phase.

This would be a big step toward parallel dataflow parallel programming, since Map and Reduce are two very powerful primitives for this purpose.

There are some other primitives that might be useful. One would be broadcast. This would take the output from one processing node during one phase and send it to all the other nodes (in the next phase). Let's just say that using broadcast, it would be much easier to send variables around for processing. No more defining weird variables using "set" in the main program, and then parsing them in setup() functions. No more setting up temporary storage space, shared by all the processors. No more using HDFS to store small serial files, local to only one node. Just send data through a broadcast, and it goes everywhere. (If the broadcast is running on more than one node, then the results would be concatenated together, everywhere.)

And, if I had a broadcast, then my two-pass row number code (here) would only require one pass.

I think Hadoop already supports having multiple different input files into one reduce operator. This is quite powerful, and a much superior way of handling join processing.

It would also be nice to have a final sort operator. In the real world, people often do want sorted results.

In conclusion, parallel dataflows are a very powerful, expressive, and efficient way of implementing complex data processing tasks. Relational databases use dataflow engines for their processing. Using non-procedural languages such as SQL, the power of dataflows are hidden from the user -- and, some relatively simple dataflow constructs can be quite difficult to express in SQL.

Hadoop is a powerful system that emulates parallel dataflow programming. Any step in a dataflow can be implemented using a MapReduce pass -- but this requires reading, writing, sorting, and sending the data multiple times. With a few more features, Hadoop could efficiently implement parallel dataflows. I feel this would be a big boost to both performance and utility, and it would leverage the power already provided by the Hadoop framework.

Tuesday, January 5, 2010

The current issue of Communications of the ACM has articles on MapReduce and relational databases. One, MapReduce a Flexible Data Processing Tool, explains the utility of MapReduce by two Google fellows -- appropriate authors, since Google invented the parallel MapReduce paradigm.

The second article, MapReduce and Parallel DBMSs: Friend or Foe, is written by a team of authors, with Michael Stonebraker listed as the first author. I am uncomfortable with this article, because the article purports to show the superiority of a particular database system, Vertica, without mentioning -- anywhere -- that Michael Stonebraker is listed as the CTO and Co-Founder on Vertica's web site. For this reason, I believe that this article should be subject to much more scrutiny.

Before starting, let me state that I personally have no major relationships with any of the database vendors or with companies in the Hadoop/MapReduce space. I am an advocate of using relational databases for data analysis and have written a book called Data Analysis Using SQL and Excel. And, over the past three months, I have been learning Hadoop and MapReduce, as attested to by numerous blog postings on the subject. Perhaps because I am a graduate of MIT ('85), I am upset that Michael Stonebraker uses his MIT affiliation for this article, without mentioning his Vertica affiliation.

The first thing I notice about the article is the number of references to Vertica. In the main text, I count nine references to Vertica, as compared to thirteen mentions of other databases:

Aster (twice)

DataAllegro (once)

DB2 (twice)

Greenplum (twice)

Netezza (once)

ParAccel (once)

PostgreSQL (once)

SQL Server (once)

Teradata (once)

The paper describes a study which compares Vertica, another database, and Hadoop on various tasks. The paper never explains how these databases were chosen for this purpose. Configuration issues for the other database and Hadoop are mentioned. The configuration and installation of Vertica -- by the absence of problems -- one assumes is easy and smooth. I have not (yet) read the paper cited, which describes the work in more detail.

Also, the paper never describes costs for the different system, which is a primary driver of MapReduce. The software is free and runs on cheap clusters of computers, rather than expensive servers and hardware. For a given amount of money, MapReduce may provide a much faster solution, since it can support much larger hardware environments.

The paper never describes issues in the loading of data. I assume this is a significant cost for the databases. Loading the data for Hadoop is much simpler . . . since it just reads text files, which is a common format.

From what I can gather, the database systems were optimized specifically for the tasks at hand, although this is not explicitly mentioned anywhere. For instance, the second tasks is a GROUP BY, and I suspect that the data is hash partitioned by the GROUP BY clause.

There are a few statements that I basically disagree with.

"Lastly, the reshuffle that occurs between the Map and Reduce tasks in MR is equivalent to a GROUP BY operation in SQL." The issue here at first seems like a technicality. In a relational database, an input row can only into one group. MR can output multiple records in the map stage, so a single row can go into multiple "groups". This functionality is important for the word count example, which is the canonical MapReduce example. I find it interesting that this example is not included in the benchmark.

"Given this, parallel DBMSs provide the same computing model as MR, with the added benefit of using a declarative language (SQL)." This is not true in several respects. First, MapReduce does have associated projects for supporting declarative languages. Second, in order for SQL to support the level of functionality that the authors claim, they need to use user defined functions. Is that syntax declarative?

More importantly, though, is that the computing model really is not exactly the same. Well, with SQL extensions such as GROUPING SETs and window functions, the functionality does come close. But, consider the ways that you can add a row number to data (assuming that you have no row number function built-in) using MapReduce versus traditional SQL. Using MapReduce you can follow the two-phase program that I described in an earlier posting. With traditional SQL, you have to do a non-equi-self join. MapReduce has a much richer set of built-in functions and capabilities, simply because it uses java, an established programming language with many libraries.

On the other hand, MapReduce does not have a concept of "null" built-in (although users can define their own data types and semantics). And, MapReduce handles non-equijoins poorly, because the key is used to direct both tables to the same node. In effect, you have to limit the MapReduce job to one node. SQL can still parallelize such queries.

"[MapReduce] still requires user code to parse the value portion of the record if it contains multiple attributes." Well, parse is the wrong term, since a Writable class supports binary representations of data types. I describe how to create such types here.

I don't actually feel qualified to comment on many of the operational aspects of optimizing Hadoop code. I do note that the authors do not explain the main benefit of Vertica, which is the support of column partitioning. Each column is stored separate, which makes it possible to apply very strong compression algorithms to the data. In many cases, the Vertica data will fit in memory. This is a huge performance boost (and one that another vendor, Paracel takes advantage of).

In the end, the benchmark may be comparing the in-memory performance of a database to general performance for MapReduce. The benchmark may not be including the ETL time for loading the data, partitioning data, and building indexes. The benchmark may not have allocated optimal numbers of map and reduce jobs for the purpose. And, it is possible that the benchmark is unbiased and relational databases really are better.

A paper that leaves out the affiliations between its authors and the vendors used for a benchmark is only going to invite suspicion.

Saturday, January 2, 2010

To set out to learn Hadoop and Map/Reduce, I tackled several different problems. The last of these problems is the challenge of normalizing data, a concept from the world of relational databases. The earlier problems were adding sequential row numbers and characterizing values in the data.

This posting describes data normalization, explains how I accomplished it in Hadoop/MapReduce, and some tricks in the code. I should emphasize here that the code is really "demonstration" code, meaning that I have not worked hard on being sure that it always works. My purpose is to demonstrate the idea of using Hadoop to do normalization, rather than producing 100% working code.

What is Normalization and Why Do Want To Do It?

Data normalization is the process of extracting values from a single column and placing them in a reference table. The data used by Hadoop is typically unnormalized, meaning that data used in processing is in a single record, so there is no need to join in reference tables. In fact, doing a join is not obvious using the MapReduce primitives, although my understanding is that Hive and Pig -- two higher level languages based on MapReduce -- do incorporate this functionality.

Why would we want to normalize data? (This is a good place to plug my book Data Analysis Using SQL and Excel, which explains this concept in more detail in the first chapter.) In the relational world, the reason is something called "relational integrity", meaning that any particular value is stored in one, and only one, place. For instance, if the state of California were to its name, we would not want to update every record from California. Instead, we'd rather go to the reference table and just change the name to the new name, and the data field contains a state id rather than the state name itself. Relational integrity is particularly important when data is being updated.

Why would we want to normalize data used by Hadoop? There are two reasons. The first is that we may be using Hadoop processing to load a relational database -- one that is already designed with appropriate reference tables. This is entirely reasonable, relational databases are an attractive way to "publish" results from complex data processing since they are better for creating end-user reports and building interactive GUI interfaces.

The second reason is performance. Extracting long strings and putting them in a separate reference table can significantly reduce the storage requirements for the data files. By far, most of the space taken up in typical log files, for instance, consists of long URIs (what I used to call URLs). When processing the log files, we might want to extract some features from the URIs, but keeping the entire string just occupies a lot of space -- even in a compressed file.

The Process of Normalizing Data

Normalizing data starts with data structures. The input records are assumed to be in a delimited format, with the column names in the first row (or provided separately, although I haven't tested that portion of the code yet). In addition, there is a "master" id file that contains the following columns:

id -- a unique id for every value by column.

column name -- the name of the column.

value -- the id in the column.

count -- the total number of times the value as so far occurred.

This is a rudimentary reference file. I could imagine, for instance, having more information than just the count as summary information -- perhaps the first and last date when the value occurs, for instance.

What happens when we normalize data? Basically, we look through the data file to find new values in each column being normalized. We append these new values into the master id file, and then go back to the original data and replace the values with the ids.

Hadoop is a good platform for this for several reasons. First, because the data is often stored as text files, the values and the ids have the same type -- text strings. This means that the file structures remain the same. Second, Hadoop can process multiple columns at the same time. Third, Hadoop can use inexpensive clusters and free software for this task, rather than relying on databases and tools, which are often more expensive.

How To Normalize Data Using Hadoop/MapReduce

The normalization process has six steps. Most of these correspond to a single Map-Reduce pass.

Step 1: Extract the column value pairs from the original data.

This step explodes the data, by creating a new data set with multiple rows for each row in the original data. Each output row contains a column, a value, and the number of times the value appears in the data. Only columns being normalized are included in the output.

This step also saves the column names for the data file in a temporary file. I'll return to why this is needed in Step 6.

Step 2: Extract column-value Pairs Not In Master ID File

This step compares the column-value pairs produced in the first step with those in the master id file. This step is interesting, because it reads data from two different data source formats -- the master id file and the results from Step 1. Both sets of data files use the GenericRecord format.

To identify the master file, the map function looks at the original data to see whether "/master" appears in the path. Alternative methods would be to look at the GenericRecord that is created or to use MultipleInputs (which I didn't use because of a warning on Cloudera's web site).

Step 3: Calculate the Maximum ID for Each Column in the Master File

This is a very simple Map-Reduce step that simply gets the maximum id for each column. New ids that are assigned will be assigned one more than this value.

This is an instance where I would very much like to have two different reduces following a map step. If this were possible, then I could combine this step with step 2.

Step 4: Calculate a New ID for the Unmatched Values

This is a two step process that follows the mechanism for adding row numbers discussed in one of my earlier posts, with one small modification. The final result has the maximum id value from Step 3 added onto it, so the result is a new id rather than just a row number.

Step 5: Merge the New Ids with the Existing Master IDs

This step merges in the results from Step 4 with the existing master id file. Currently, the results are placed into another directly. Eventually, they could simply override the master id file.

Because of the structure of the Hadoop file system, the merge could be as simple as copying the file with the new ids into the appropriate master id data space. However, this would result in an unbalanced master id file, which is probably not desirable for longer term processing.

Step 6: Replace the Values in the Original Data with IDs

This final step replaces the values with ids -- the actual normalization step. This is a two part process. The map phase of the first part takes both the original data and the master key file. All the column value pairs are exploded from the original data, as in Step 1, with the output consisting of:

key: :

value: , ,

The first part ("expect" or "nomaster") is an indicator of whether this column should be normalized (that is, whether or not to expect a master id). The second field identifies the original data record, which is uniquely identified by the partition id and row number within that partition. The third is the column number in the row.

The master records are placed in the format:

key: :

value: "master",

The reduce then reads through all the records for a given column-value combination. If one of them is a master, then it outputs the id for all records. Otherwise, it outputs the original value.

The last phase simply puts the records back together again, from their exploded form. The one trick here is that the metadata is read from a local file.

One trick that I use in Step 4, for the phase 1 map, makes the code more efficient. This phase of the computation extracts the maximum row number for each column. Instead of passing all the row numbers to a combine or reduce function, it saves them in a local hash-map data structure. I then use the cleanup() routine in the map function to output the maximum values.

Often the master code needs to pass variables to the map/reduce jobs. The best way to accomplish this is by using the "set" mechanism in the Configuration object. This allows variables to be assigned a string name. The names of all the variables that I use are stored in constants that start with PARAMETER_, defined at the beginning of the Normalize class.

In some cases, I need to pass arrays in, for instance, when passing in the list of column that are to be normalized. In this case, one variable gives the number of values ("normalize.usecolumns.numvals"). Then each value is stored in a variable such as "normalize.usecolumns.0" and "normalize.usecolumns.1" and so on.

Some of the important processing actually takes place in the master loop, where results are gathered and then passed to subsequent steps using this environment mechanism.

The idea behind the GenericRecord class is pretty powerful, with the column names at the top of the file. GenericRecords make it possible to read multiple types of input in the same map class, for instance, which is critical functionality for combining data from two different input streams.

However, the Map-Reduce framework does not really recognize these column names as being different, once generic records are placed in a sequence file. The metadata has to be passed somehow.

When the code itself generates the metadata, this is simple enough. A function is used to create the metadata, and this function is used in both the map and reduce phases.

A bigger problem arises with the original data. In particular, Step 6 of the above framework re-creates the original records, but it has lost the column names, which poses a conundrum. The solution is to save the original metadata in Step 1, which first reads the records. This metadata is then passed into Step 6.

In this code, this is handled by simply using a file. The first map partition of Step 1 writes this file (this partition is used to guarantee that the file is written exactly once). The last reduce in Step 6 then reads this file.

This mechanism works, but is not actually the preferred mechanism, because all the reduce tasks in Step 6 are competing to read the same file -- a bottleneck.

A better mechanism is for the master program to read the file and to place the contents in variables in the jar file passed to the map reduce tasks. Although I do this for other variables, I don't bother to do this for the file.